| # of Responses | # of Students | Response Rate |
|---|---|---|
| 27 | 29 | 93.1% |
DATA1220-55, Fall 2024
2024-09-16
Read Section 2.2.5 in your OpenIntro Statistics text book
Read this opinion piece on the use of pie charts to visualize proportions
Answer the question on Campuswire for additional participation points
It will not be available after Friday 9/20/24
Instructions (homework2_instructions.pdf), a Quarto markdown template (homework2_template.qmd), and an example HTML output (homework2_example.html) are available for download under Chapter 2 on the Modules page in Canvas.
Upload TWO (2) documents to Homework 2 on the Assignments page in Canvas by Friday 9/20/2024 by 6:00pm: homework2_yourlastname.qmd and homework2_yourlastname.html
Video walk-through of Homework 2 under Tutorials on the Modules page in Canvas. Make sure you’re caught up on the video walk-through of homework 1.
You now have experience making professional-looking HTML documents that embed statistical programming and data visualizations into traditional text.
You started a project in RStudio
You transferred files from your Downloads folder to your project folder
You edited a Quarto Markdown Document (i.e. QMD, a file ending in .qmd)
You rendered your Quarto Markdown Document into an HTML file (i.e. a file ending in .html).
You have done basic statistical analysis.
You loaded data into a project’s environment in RStudio
You inspected that data to determine the data type of the variables
You created a codebook describing the variable
You analyzed the relationship between 2 numerical variables
You communicated your findings about that relationship
Effectively describe numerical distributions
Select the appropriate summary statistics based on distribution shape
Match numerical distributions to their summary statistics
Calculate proportions from a contingency table
“This homework is due by 6:00pm on Friday, 9/20/24. No credit will be lost for assignments received by 7:00pm to account for issues with uploading. 10% of the points will be deducted from assignments received by 9:00am on Saturday, 9/21/24. Assignments turned in after this point are only eligible for 50% credit, so it benefits you to turn in whatever you have completed by the due date.”
Read the textbook. Many of you are asking for additional examples. Luckily, there are tons we didn’t go over in the textbook.
Look at the homework early. Only 1 person has looked at the homework since I posted it. Make sure you leave enough time to get help if you need it.
Ask a question on our Campuswire class feed. I’m only one person, and I may not be able to give you a prompt answer. However, the 20+ other people in the class might be able to.
Come to office hours. I will be available after class today (Monday 9/23/2024) and Wednesday 9/25/2024 from 2:30pm - 4:00pm. If you cannot make it, reach out to me to try and schedule an appointment.
Contingency tables: counts and proportions (frequencies)
Visualizing frequencies: bar plots, mosaic plots
Describing numerical relationships: linear vs nonlinear, strong vs weak
Visualizing 3+ variables
Review Survey 1 Results
Sarah’s Objectives
Introduce Chapter 3 on Probability
| # of Responses | # of Students | Response Rate |
|---|---|---|
| 27 | 29 | 93.1% |
| pronouns | 2024 | 2025 | 2026 | 2027 | 2028 | Total |
|---|---|---|---|---|---|---|
| He/Him | 0 | 1 | 2 | 6 | 0 | 9 |
| She/Her | 1 | 2 | 3 | 7 | 3 | 16 |
| Total | 1 | 3 | 5 | 13 | 3 | 25 |
| pronouns | 2024 | 2025 | 2026 | 2027 | 2028 | Total |
|---|---|---|---|---|---|---|
| He/Him | 0.0% | 11.1% | 22.2% | 66.7% | 0.0% | 100.0% |
| She/Her | 6.2% | 12.5% | 18.8% | 43.8% | 18.8% | 100.0% |
| Total | 4.0% | 12.0% | 20.0% | 52.0% | 12.0% | 100.0% |
| pronouns | 2024 | 2025 | 2026 | 2027 | 2028 | Total |
|---|---|---|---|---|---|---|
| He/Him | 0.0% | 33.3% | 40.0% | 46.2% | 0.0% | 36.0% |
| She/Her | 100.0% | 66.7% | 60.0% | 53.8% | 100.0% | 64.0% |
| Total | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
| excited | 1 | 2 | 3 | 4 | Total |
|---|---|---|---|---|---|
| 1 | 3 | 0 | 1 | 0 | 4 |
| 2 | 3 | 0 | 0 | 0 | 3 |
| 3 | 6 | 0 | 1 | 0 | 7 |
| 4 | 6 | 1 | 0 | 0 | 7 |
| 5 | 0 | 1 | 1 | 1 | 3 |
| 6 | 2 | 0 | 0 | 1 | 3 |
| Total | 20 | 2 | 3 | 2 | 27 |
| excited | 1 | 2+ | Total |
|---|---|---|---|
| 1 | 3 | 1 | 4 |
| 2 | 3 | 0 | 3 |
| 3 | 6 | 1 | 7 |
| 4 | 6 | 1 | 7 |
| 5+ | 2 | 4 | 6 |
| Total | 20 | 7 | 27 |
Be available. I will check in with our next survey about office hour times.
Provide real world examples and practice problems.
Help with coding and make how-to videos.
Be patient and give thorough explanations.
Be a good teacher. Unclear how.
Get students to like statistics. Trying!
Be fun, helpful, passionate, and engaging.
Promote good vibes. I need your help!
Many people remarked that their favorite class was their favorite because of the people in it, who were engaged, had fun, and participated.
Come prepared
Ask and answer questions in class
Ask and answer questions on Campuswire
Don’t be afraid to make mistakes
Define probability, random processes, and the law of large numbers
Describe the sample space for disjoint and non-disjoint outcomes
Calculate probabilities using the General Addition and Multiplication Rules
Create a probability distribution for disjoint outcomes
What does the word probability mean to you?
“Highly likely”
“Probably”
“About even”
“Almost no chance”
Did your estimate fall within these ranges? Are these ranges reasonable?
Frequentist Definition
The proportion of times that a particular outcome would occur if we observed a random process an infinite number of times.
A random process is one where you know which outcomes are possible (i.e. the sample space) but you don’t know which outcome comes next
Examples of a random process: coin toss, die roll, stock market
Both Apple and Spotify took steps to make their “shuffle” features less random after complaints from users.
January 11, 2005 – Apple releases the iPod Shuffle, a small device capable only of playing music randomly
July 2011 – Spotify launches in the United States using the Fisher-Yates Algorithm, which is like picking tickets out of a hat until no more remain
The human brain is good at finding patterns in noise, even when there are none
If an artist is repeated “too soon”, the listener doesn’t feel the order is random
We perceive a “random” distribution as also being “uniform” and “fair”
Songs not evenly distributed across albums and artists on a playlist
Some albums/artists may play more often than others because they have more songs
Artists/albums with more songs also more likely to play sequentially
A true random shuffle might play the same artist multiple times in a row
Each time the song changes, every song on the playlist is eligible to be played next
Does not matter if the song was just played
Does not matter who the artist is
We call this sampling with replacement.
Like drawing a playing card, looking at it, then putting it back in the deck before the next draw.
Repetition of outcomes is possible.
There is our “observed” probability that the next song is by Chappell Roan
There is some “true” real-world probability that the next song is by Chappell Roan
The sample space is the total collection of possible outcomes for a random process.
Die rolls: 1, 2, 3, 4, 5, 6
Coin flips: heads, tails
Stock market: up, down, no change
Here, the sample space is the songs on the playlist (n = 50) and the artists who perform them (n = 26).
Probabilities are proportions, or the number of observations with a particular value divided by the total number of observations (\(n\)).
Proportions range from 0 (no observations in data) to 1 (all observations in data)
Also may be a percentage, ranging from 0% to 100% (multiply proportion by 100)
Should we listen to 1 song?
Should we listen to 5 songs?
Should we listen to 10 songs?
Should we listen to 100 songs?
Should we listen to 200 songs?
As more observations are collected, the sample proportion \(\hat{p}_n\) of a particular outcome approaches the population proportion \(p\) of that outcome.
Outcomes are disjoint or mutually exclusive if they cannot both happen at the same time
Non-disjointoutcomes can occur at the same time.
DATA1220-55 Fall 2024, Class 08 | Updated: 2024-09-16 | Canvas | Campuswire